When personal computers and television receivers display an image sent from a source, it is necessary to perform a pixel number conversion on the original image by using image processing apparatuses in a desired conversion rate.
Representative examples of the pixel number conversion method used for such type of image processing apparatus include cubic interpolation and linear interpolation, which perform interpolation using luminous values of pixels around the pixel to be converted. Also included in the representative examples is nearest neighborhood interpolation that uses a gradation value of the nearest pixel in the coordinates as it is.
Personal computers and television receivers need to display both of natural images and letter images. These days, there is intensified needs for each type of image to be easy to be seen on a screen after conversion.
Here, according to the cubic interpolation and the linear interpolation, the barycenter of the gradation value will be maintained after conversion. Therefore, these two interpolation methods are suitable for natural images since the converted images will look natural without image blurriness as a whole. However, these two interpolation methods are not ideal for letter images (i.e. images that have sharp edges such as letter or graphics) since the edge parts will be blurred.
On the other hand, nearest neighborhood interpolation is not ideal for natural images since the converted images will not look natural. But it is suitable to be used in conversion of letter images since the converted edge parts will not look blurry. However there is one problem, that is, line-widths as a whole will not be uniform. That is, for lines whose line-widths used to be the same will be converted into lines with different widths depending on the location of the lines.
In particular, many of original images treated by personal computers are letter or charts that are composed of fine lines having line-width of one pixel. These images will yield, if nearest neighborhood interpolation is applied, images that have lines of different line-width from each other, which seems strange to viewers.
There have been developed an image processing apparatus that can select between the mentioned cubic interpolation and the nearest neighborhood interpolation, for displaying both of the natural and letter images.
For example, Japanese Laid-open Patent Application No. H11-203467 discloses an image processing apparatus that consists of a waveform identifying circuit 31, a selector 32, a cubic interpolation processing circuit 33, and a nearest neighborhood interpolation processing circuit 34 (see FIG. 23).
In the stated apparatus, the waveform identifying circuit 31 identifies an input image signal 35, then, normally the selector 32 selects the cubic interpolation processing circuit 33. However, if the edge part is identified as a cascade waveform, the selector 32 selects the nearest neighborhood interpolation circuit 34. The apparatus enables to apply cubic interpolation to natural images, and to apply nearest neighborhood interpolation to the edge-part images. This method enables to obtain pixel image suitable for natural images, and to obtain images with reduced blurriness for edge-part images.
However, even with the above apparatus, the problem of the inconsistency in line-widths still exists.
The above problem is explained with reference to FIG. 24.
FIGS. 24A, 24B, and 24C depict three examples for performing interpolation in the horizontal direction. FIG. 24A and FIG. 24C use nearest neighborhood interpolation, and FIG. 24B uses cubic interpolation. This figure depicts the manner in which an input image consisting of pixel coordinates H1–H5 positioned in the horizontal direction is converted into pixel coordinates D1–D6 also in the horizontal direction.
In FIGS. 24A, 24B, and 24C, a white pixel represents high luminance (a luminous value of 255), a black pixel represents low luminance (a luminous value of 0), a double-hatched pixel represents a medium low luminance (a luminous value of 64), and a black-dot hatched pixel represents medium luminance (a luminous value of 128).
In FIG. 24A and FIG. 24B, H3 in the input image represents low luminance, and the other pixels H1, H2, H4, and H5 represent high luminance. In the stated examples, H3 is the line-forming pixel. On the other hand, in FIG. 24C, H4 in the input image is the line-forming pixel.
When performing cubic interpolation as in FIG. 24B, each of the pixels after conversion: D2, D3, D4, and D5 is interpolated using the four pixels before conversion as references. For example, the coordinates D3 in the output image are interpolated using the coordinates H1–H4 as a reference.
In this case, in the input image, a line has a width of one pixel (H3), whereas after conversion, the width is magnified to be four pixels (D2–D5), and becomes blurry due to the reduced luminous difference between D2–D5 and the pixels around D2–D5 (i.e. D1, D6).
In both FIGS. 24A and 24C, nearest neighborhood interpolation is performed. In FIG. 24A in which H3 is the line-forming pixel, D3 and D4 will be the line-forming pixels in the output image. On the other hand, in FIG. 24C in which H4 is the line-forming element, only D5 will be the line-forming pixel in the output image. As such, two lines of a same line-width are converted into lines of different widths with each other: in FIG. 24A, in two-pixel width; and in FIG. 24C, in one-pixel width.